An Efficient Epileptic Seizure Detection Technique using Discrete Wavelet Transform and Machine Learning Classifiers

This paper presents an epilepsy detection method based on discrete wavelet transform (DWT) and Machine learning classifiers. Here DWT has been used for feature extraction as it provides a better decomposition of the signals in different frequency bands. At first, DWT has been applied to the EEG signal to extract the detail and approximate coefficients or different sub-bands. After the extraction of the coefficients, principal component analysis (PCA) has been applied on different sub-bands and then a feature level fusion technique is used to extract the important features in low dimensional feature space. Three classifiers namely: Support Vector Machine (SVM) classifier, K-Nearest-Neighbor (KNN) classifier, and Naive Bayes (NB) Classifiers have been used in the proposed work for classifying the EEG signals. The proposed method is tested on Bonn databases and provides a maximum of 100% recognition accuracy for KNN, SVM, NB classifiers.Comment: Accepted in International Conference on Smart Technologies for Sustainable Development (ICSTSD2021

Two-stage motion artefact reduction algorithm for electrocardiogram using weighted adaptive noise cancelling and recursive Hampel filter

The presence of motion artefacts in ECG signals can cause misleading interpretation of cardiovascular status. Recently, reducing the motion artefact from ECG signal has gained the interest of many researchers. Due to the overlapping nature of the motion artefact with the ECG signal, it is difficult to reduce motion artefact without distorting the original ECG signal. However, the application of an adaptive noise canceler has shown that it is effective in reducing motion artefacts if the appropriate noise reference that is correlated with the noise in the ECG signal is available. Unfortunately, the noise reference is not always correlated with motion artefact. Consequently, filtering with such a noise reference may lead to contaminating the ECG signal. In this paper, a two-stage filtering motion artefact reduction algorithm is proposed. In the algorithm, two methods are proposed, each of which works in one stage. The weighted adaptive noise filtering method (WAF) is proposed for the first stage. The acceleration derivative is used as motion artefact reference and the Pearson correlation coefficient between acceleration and ECG signal is used as a weighting factor. In the second stage, a recursive Hampel filter-based estimation method (RHFBE) is proposed for estimating the ECG signal segments, based on the spatial correlation of the ECG segment component that is obtained from successive ECG signals. Real-World dataset is used to evaluate the effectiveness of the proposed methods compared to the conventional adaptive filter. The results show a promising enhancement in terms of reducing motion artefacts from the ECG signals recorded by a cost-effective single lead ECG sensor during several activities of different subjects

Event-related EEG analysis : b simple solutions of complex computations

PhD ThesisThe value of EEG as a non-invasive technique for studying the time course and frequency composition of neuronal signals is well established. However, to date there is still no gold standard methodology for its analysis. Since the introduction of the technique many methodologies for artefact removal and signal isolation have been developed but their performance is often only assessed, against other methodologies, using simulated data with known and controlled artefacts and limited variance. Furthermore, these studies often only address a single stage in the entire analysis pipeline and do not consider the affect different preprocessing techniques might have upon the effectiveness of different signal analysis methodologies. To address this issue this thesis approaches the assessment of 4 different signal analysis methodologies using real-world-data, from two different stimulus evoked potential studies, and an EEG analysis pipeline that systematically applies and adjusts various preprocessing techniques before subsequent signal analysis. This semi-automated process can be broken down into two stages. Firstly, multiple configurations of a Preprocessing Optimisation Pipeline (POP) were performed to address three main causes of artefactual noise (1) electrical line noise, (2) non-neuronal potentials (low frequency drifts and muscle artefacts), and (3) ocular artefacts (blinks and saccades). Within the final stages of the POP data quality was assessed for each participant and poorly preprocessed participant datasets were excluded from further analysis based upon either a novel maximum baseline variability threshold criterion or a standard minimum epoch number threshold approach. Lastly, the data was passed onto a Signal Analysis Pipeline (SAP) which estimated the amplitude of task-specific signals of interest through one of four methodologies (1) grand average informed peak detection (GA-PD), (2) individual average peak detection (IAPD), (3) independent component analysis informed peak detection (ICA-PD) or (4) component of interest peak detection (COIPD). The effectiveness of each of the different preprocessing and signal analysis strategies were then assessed based upon observing the changes within task-specific outcome statistics

A new ICA-based fingerprint method for the automatic removal of physiological artifacts from EEG recordings

Background: EEG may be affected by artefacts hindering the analysis of brain signals. Data-driven methods like independent component analysis (ICA) are successful approaches to remove artefacts from the EEG. However, the ICA-based methods developed so far are often affected by limitations, such as: the need for visual inspection of the separated independent components (subjectivity problem) and, in some cases, for the independent and simultaneous recording of the inspected artefacts to identify the artefactual independent components; a potentially heavy manipulation of the EEG signals; the use of linear classification methods; the use of simulated artefacts to validate the methods; no testing in dry electrode or high-density EEG datasets; applications limited to specific conditions and electrode layouts. Methods: Our fingerprint method automatically identifies EEG ICs containing eyeblinks, eye movements, myogenic artefacts and cardiac interference by evaluating 14 temporal, spatial, spectral, and statistical features composing the IC fingerprint. Sixty-two real EEG datasets containing cued artefacts are recorded with wet and dry electrodes (128 wet and 97 dry channels). For each artefact, 10 nonlinear SVM classifiers are trained on fingerprints of expert-classified ICs. Training groups include randomly chosen wet and dry datasets decomposed in 80 ICs. The classifiers are tested on the IC-fingerprints of different datasets decomposed into 20, 50, or 80 ICs. The SVM performance is assessed in terms of accuracy, False Omission Rate (FOR), Hit Rate (HR), False Alarm Rate (FAR), and sensitivity (p). For each artefact, the quality of the artefact-free EEG reconstructed using the classification of the best SVM is assessed by visual inspection and SNR. Results: The best SVM classifier for each artefact type achieved average accuracy of 1 (eyeblink), 0.98 (cardiac interference), and 0.97 (eye movement and myogenic artefact). Average classification sensitivity (p) was 1 (eyeblink), 0.997 (myogenic artefact), 0.98 (eye movement), and 0.48 (cardiac interference). Average artefact reduction ranged from a maximum of 82% for eyeblinks to a minimum of 33% for cardiac interference, depending on the effectiveness of the proposed method and the amplitude of the removed artefact. The performance of the SVM classifiers did not depend on the electrode type, whereas it was better for lower decomposition levels (50 and 20 ICs). Discussion: Apart from cardiac interference, SVM performance and average artefact reduction indicate that the fingerprint method has an excellent overall performance in the automatic detection of eyeblinks, eye movements and myogenic artefacts, which is comparable to that of existing methods. Being also independent from simultaneous artefact recording, electrode number, type and layout, and decomposition level, the proposed fingerprint method can have useful applications in clinical and experimental EEG settings

Comparing the Performance of Popular MEG/EEG Artifact Correction Methods in an Evoked-Response Study

We here compared results achieved by applying popular methods for reducing artifacts in magnetoencephalography (MEG) and electroencephalography (EEG) recordings of the auditory evoked Mismatch Negativity (MMN) responses in healthy adult subjects. We compared the Signal Space Separation (SSS) and temporal SSS (tSSS) methods for reducing noise from external and nearby sources. Our results showed that tSSS reduces the interference level more reliably than plain SSS, particularly for MEG gradiometers, also for healthy subjects not wearing strongly interfering magnetic material. Therefore, tSSS is recommended over SSS. Furthermore, we found that better artifact correction is achieved by applying Independent Component Analysis (ICA) in comparison to Signal Space Projection (SSP). Although SSP reduces the baseline noise level more than ICA, SSP also significantly reduces the signal-slightly more than it reduces the artifacts interfering with the signal. However, ICA also adds noise, or correction errors, to the wave form when the signal-to-noise ratio (SNR) in the original data is relatively low-in particular to EEG and to MEG magnetometer data. In conclusion, ICA is recommended over SSP, but one should be careful when applying ICA to reduce artifacts on neurophysiological data with relatively low SNR.Peer reviewe

Characterization and filtering of electroencephalogram contaminated by electromyography of facial muscles

The Electroencephalogram (EEG) has been the most preferred way of recording brain activity due to its noninvasiveness and affordability benefits. Information estimated from EEG has been employed broadly, e.g., for diagnosis or as an input signal to Brain-Computer Interfaces (BCI). Nevertheless, the EEG is prone to artifacts including non-brain physiological activities, such as eye blinking and the contraction of the muscles of the scalp. Some applications such as BCI systems may occasionally be associated with frequent contractions of muscles of the head corrupting the EEG-based control signal. This requires the application of several filtering techniques. However, the gold standard techniques for signal filtering still contain limitations, such as the incapacity of eliminating noise in all EEG channels. For this reason, besides studying and applying filtering techniques, it is necessary to understand the contamination from electromyogram (EMG) along the scalp. Several studies concluded that EMG artifact contaminates the EEG at frequencies beginning at 15 Hz on the topographic distribution of the energy that encompasses practically the entire scalp. Thus, the present work aims to quantitatively estimate EMG noise in 16 bipolar channels of EEG distributed along the scalp according to the 10-20 system. This estimation was based on an experimental protocol considering the simultaneous acquisition of EEG and EMG of five facial muscles sampled at 5 kHz. The protocol consisted of activating facial muscles while listening to 15 beep sounds. The evaluated muscles were frontal, masseter, zygomatic, orbicularis oculi, and orbicularis oris. The mean power of the EEG contaminated by EMG of facial muscle contractions was compared between the periods of muscle contraction and non-contraction. The results show that EMG contamination from frontal and masseter muscles are present over the scalp with an increase from 63.5 μV2 to 816 μV2 and from 118.3 μV2 to 5,617.9 μV2, respectively. Also, this work proposes a technique for EMG artifact removal that is less sensitive to low SNR as the current gold standard techniques. The proposed method, so-called EMDRLS, employs Empirical Mode Decomposition (EMD) to generate an EMG noise reference to an adaptive Recursive Least Squares (RLS) filter. To test the EMDRLS method, EEG signals were collected from 10 healthy subjects during the controlled execution of successive facial muscular contractions. The experimental protocol considered the isolated activation of the masseter and frontal muscles. EEG corrupted signals were filtered by the EMDRLS method considering distinct SNRs. The results were compared to traditional approaches: Wiener, Wavelet, EMD, and a hybrid wavelet-RLS filtering method. The following performance metrics were considered in the comparative evaluation: (i) SNR of the contaminated signal; (ii) the root mean square error (RMSE) between the power spectrum of artifact-free and filtered EEG epochs; (iii) the spectral preservation of brain rhythms (i.e., delta, theta, alpha, beta, and gamma) of filtered signals. For EEG signals with SNR below -10dB, the EMDRLS method yielded filtered EEG signals with SNR varying from 0 to 10 dB. The technique reduced the RMSE of frontal channels from 1.202 to 0.043, which are the source of the most corrupted EEG signals. The Kruskal-Wallis test and the Tukey-Kramer post-hoc test (p < 0.05) confirmed the preservation of all brain rhythms given by EEG signals filtered with the EMDRLS method. The results have shown that the single-channel EMDRLS method can be applied to highly contaminated EEG signals by facial EMG signal with performance superior to that of established methods.CAPES - Coordenação de Aperfeiçoamento de Pessoal de Nível SuperiorCNPq - Conselho Nacional de Desenvolvimento Científico e TecnológicoFAPEMIG - Fundação de Amparo a Pesquisa do Estado de Minas GeraisTese (Doutorado)O Eletroencefalograma (EEG), é uma medida da atividade cerebral que ostenta as vantagens de portabilidade, baixo custo, alta resolução temporal e não invasivo. Os desafios desse exame são os artefatos de diferentes fontes que tornam a análise de dados do EEG mais difícil, e que potencialmente resulta em erros de interpretação. Portanto, é essencial para muitas aplicações médicas e práticas remover esses artefatos no pré-processamento antes de analisar os dados do EEG. Nos últimos trinta anos, vários métodos foram desenvolvidos para remover diferentes tipos de artefatos de dados de EEG contaminados; ainda assim, não há nenhum método padrão que pode ser usado de forma otimizada e, portanto, a pesquisa permanece atraente e desafiadora. Algumas aplicações, como as Interfaces Homem Computador (HCI), podem ocasionalmente estar associadas a frequentes contrações dos músculos da cabeça, corrompendo o sinal de controle baseado no EEG, requerendo a aplicação de alguma técnica de filtragem. No entanto, as técnicas padrão de ouro para filtragem de sinal ainda contêm limitações, como a incapacidade de eliminar o ruído em todos os canais EEG com relações sinal-ruído (SNR) muito baixas e quando a faixa espectral do ruído sobrepõe a do EEG, que caracteriza diversas contaminações no EEG, mas principalmente a contaminação oriunda do sinal eletromiográfico. Por esta razão, além de estudar e aplicar técnicas de filtragem, é necessário entender a contaminação do eletromiograma (EMG) ao longo do couro cabeludo. Alguns estudos concluíram que o artefato EMG contamina o EEG em frequências a partir de 15 Hz em uma distribuição topográfica que engloba praticamente todo o couro cabeludo. Assim, o presente trabalho tem como objetivo estimar quantitativamente o ruído EMG em 16 canais bipolares de EEG distribuídos ao longo do couro cabeludo de acordo com o sistema 10-20. Essa estimativa foi baseada em um protocolo experimental considerando a aquisição simultânea de EEG e EMG de cinco músculos faciais amostrados a 5 kHz. O protocolo consistiu em ativar os músculos faciais enquanto o voluntário ouvisse 15 sons de bip. Os músculos avaliados foram o frontal, masseter, temporal, zigomático, orbicular do olho e orbicular da boca. A potência média do EEG contaminado pela EMG das contrações da musculatura facial foi comparado entre os períodos de contração muscular e não contração. Os resultados mostram que a contaminação muscular do frontal e do masseter provoca um aumento de energia sobre o couro cabeludo de 63,5 μV2 para 816 μV2 e de 118,3 μV2 para 5,617,9 μV2, respectivamente. Além disso, este trabalho propõe uma técnica de remoção do artefato de EMG menos sensível a baixas SNRs que as atuais técnicas padrão ouro. O método proposto, chamado EMDRLS, emprega Decomposição do Modo Empírico (EMD) para gerar uma referência de ruído EMG a um filtro RLS (Recursive Least Squares) adaptativo. Para testar o EMDRLS, foram coletados sinais de EEG de 10 indivíduos saudáveis durante a execução controlada de sucessivas contrações musculares faciais. O protocolo experimental considerou a ativação isolada dos músculos masseter e frontal. Os sinais corrompidos por EEG foram filtrados por EMDRLS considerando SNRs distintos. Os resultados foram comparados às abordagens tradicionais: Wiener, Wavelet, EMD e um método de filtragem híbrido wavelet-RLS. As seguintes métricas de desempenho foram consideradas na avaliação comparativa: (i) SNR do sinal contaminado; (ii) o erro quadrático médio da raiz (RMSE) entre o espectro de potência das épocas de EEG filtradas e sem artefatos; (iii) a preservação espectral de ritmos cerebrais (isto é, delta, teta, alfa, beta e gama) dos sinais filtrados. Para sinais EEG com SNR abaixo de -10dB, o método EMDRLS produziu sinais EEG filtrados com SNR variando de 0 a 10 dB. A técnica reduziu o RMSE dos canais frontais de 1,202 para 0,043, que são a fonte dos sinais de EEG mais corrompidos. O teste de Kruskal-Wallis e o teste post-hoc de Tukey-Kramer (p <0,05) confirmaram a preservação de todos os ritmos cerebrais dados pelos sinais de EEG filtrados pelo método EMDRLS. Os resultados mostraram que o método EMDRLS pode ser aplicado a sinais EEG altamente contaminados por sinal facial EMG com desempenho superior ao dos métodos estabelecidos

MERLiN: Mixture Effect Recovery in Linear Networks

Causal inference concerns the identification of cause-effect relationships between variables, e.g. establishing whether a stimulus affects activity in a certain brain region. The observed variables themselves often do not constitute meaningful causal variables, however, and linear combinations need to be considered. In electroencephalographic studies, for example, one is not interested in establishing cause-effect relationships between electrode signals (the observed variables), but rather between cortical signals (the causal variables) which can be recovered as linear combinations of electrode signals. We introduce MERLiN (Mixture Effect Recovery in Linear Networks), a family of causal inference algorithms that implement a novel means of constructing causal variables from non-causal variables. We demonstrate through application to EEG data how the basic MERLiN algorithm can be extended for application to different (neuroimaging) data modalities. Given an observed linear mixture, the algorithms can recover a causal variable that is a linear effect of another given variable. That is, MERLiN allows us to recover a cortical signal that is affected by activity in a certain brain region, while not being a direct effect of the stimulus. The Python/Matlab implementation for all presented algorithms is available on https://github.com/sweichwald/MERLi

Emotional facial expressions evoke faster orienting responses, but weaker emotional responses at neural and behavioural levels compared to scenes: A simultaneous EEG and facial EMG study

AbstractIn the current study, electroencephalography (EEG) was recorded simultaneously with facial electromyography (fEMG) to determine whether emotional faces and emotional scenes are processed differently at the neural level. In addition, it was investigated whether these differences can be observed at the behavioural level via spontaneous facial muscle activity. Emotional content of the stimuli did not affect early P1 activity. Emotional faces elicited enhanced amplitudes of the face-sensitive N170 component, while its counterpart, the scene-related N100, was not sensitive to emotional content of scenes. At 220–280ms, the early posterior negativity (EPN) was enhanced only slightly for fearful as compared to neutral or happy faces. However, its amplitudes were significantly enhanced during processing of scenes with positive content, particularly over the right hemisphere. Scenes of positive content also elicited enhanced spontaneous zygomatic activity from 500–750ms onwards, while happy faces elicited no such changes. Contrastingly, both fearful faces and negative scenes elicited enhanced spontaneous corrugator activity at 500–750ms after stimulus onset. However, relative to baseline EMG changes occurred earlier for faces (250ms) than for scenes (500ms) whereas for scenes activity changes were more pronounced over the whole viewing period. Taking into account all effects, the data suggests that emotional facial expressions evoke faster attentional orienting, but weaker affective neural activity and emotional behavioural responses compared to emotional scenes

Evaluation of Data Processing and Artifact Removal Approaches Used for Physiological Signals Captured Using Wearable Sensing Devices during Construction Tasks

Wearable sensing devices (WSDs) have enormous promise for monitoring construction worker safety. They can track workers and send safety-related information in real time, allowing for more effective and preventative decision making. WSDs are particularly useful on construction sites since they can track workers’ health, safety, and activity levels, among other metrics that could help optimize their daily tasks. WSDs may also assist workers in recognizing health-related safety risks (such as physical fatigue) and taking appropriate action to mitigate them. The data produced by these WSDs, however, is highly noisy and contaminated with artifacts that could have been introduced by the surroundings, the experimental apparatus, or the subject’s physiological state. These artifacts are very strong and frequently found during field experiments. So, when there is a lot of artifacts, the signal quality drops. Recently, artifacts removal has been greatly enhanced by developments in signal processing, which has vastly enhanced the performance. Thus, the proposed review aimed to provide an in-depth analysis of the approaches currently used to analyze data and remove artifacts from physiological signals obtained via WSDs during construction-related tasks. First, this study provides an overview of the physiological signals that are likely to be recorded from construction workers to monitor their health and safety. Second, this review identifies the most prevalent artifacts that have the most detrimental effect on the utility of the signals. Third, a comprehensive review of existing artifact-removal approaches were presented. Fourth, each identified artifact detection and removal approach was analyzed for its strengths and weaknesses. Finally, in conclusion, this review provides a few suggestions for future research for improving the quality of captured physiological signals for monitoring the health and safety of construction workers using artifact removal approaches

Noise Reduction in EEG Signals using Convolutional Autoencoding Techniques

The presence of noise in electroencephalography (EEG) signals can significantly reduce the accuracy of the analysis of the signal. This study assesses to what extent stacked autoencoders designed using one-dimensional convolutional neural network layers can reduce noise in EEG signals. The EEG signals, obtained from 81 people, were processed by a two-layer one-dimensional convolutional autoencoder (CAE), whom performed 3 independent button pressing tasks. The signal-to-noise ratios (SNRs) of the signals before and after processing were calculated and the distributions of the SNRs were compared. The performance of the model was compared to noise reduction performance of Principal Component Analysis, with 95% explained variance, by comparing the Harrell-Davis decile differences between the SNR distributions of both methods and the raw signal SNR distribution for each task. It was found that the CAE outperformed PCA for the full dataset across all three tasks, however the CAE did not outperform PCA for the person specific datasets in any of the three tasks. The results indicate that CAEs can perform better than PCA for noise reduction in EEG signals, but performance of the model may be training size dependent